Packages
library(tidyverse)
library(psych)
library(lavaan)
library(semPlot)
library(knitr)
library(corrplot)
theme_set(theme_bw())
Import data
taia <- read_csv("https://github.com/angelgardt/taia/raw/master/data/taia.csv")
Preparation
Vectors of TAIA items:
pr_items <- colnames(taia)[1:10]
co_items <- colnames(taia)[11:20]
ut_items <- colnames(taia)[21:32]
fa_items <- colnames(taia)[33:42]
de_items <- colnames(taia)[43:53]
un_items <- colnames(taia)[54:65]
taia_items <- colnames(taia)[1:65]
Vector of GT items:
gt_items <- colnames(taia)[66:71]
Column names for further formatting:
col_names <- c("", "Num. of obs.", "Mean", "SD",
"Median", "Trimmed Mean", "MAD",
"Min", "Max", "Range",
"Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
"Average interitem correlation", "S/N",
"Alpha SE", "Scale Mean", "Total Score SD",
"Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
"Std Cor",
"Cor Overlap Corrected",
"Cor if drop",
"Difficulty", "SD")
alpha_drop <- c("Alpha", "Standardized Alpha",
"Guttman's Lambda 6", "Average interitem correlation",
"S/N", "Alpha SE", "Var(r)","Median interitem correlation")
Exploratory analysis
TAIA descriptive statistics
taia %>%
select(all_of(pr_items)) %>%
describe() %>%
kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)
Table 1: Predictability
| pr01 |
1 |
513 |
2.84 |
1.00 |
3 |
2.88 |
1.48 |
0 |
5 |
5 |
-0.32 |
0.44 |
0.04 |
| pr02 |
2 |
513 |
2.75 |
0.98 |
3 |
2.79 |
1.48 |
0 |
5 |
5 |
-0.21 |
0.12 |
0.04 |
| pr03 |
3 |
513 |
2.86 |
1.04 |
3 |
2.89 |
1.48 |
0 |
5 |
5 |
-0.16 |
-0.04 |
0.05 |
| pr04 |
4 |
513 |
2.81 |
1.07 |
3 |
2.85 |
1.48 |
0 |
5 |
5 |
-0.19 |
-0.02 |
0.05 |
| pr05 |
5 |
513 |
2.25 |
1.21 |
2 |
2.26 |
1.48 |
0 |
5 |
5 |
0.02 |
-0.32 |
0.05 |
| pr06 |
6 |
513 |
3.05 |
1.08 |
3 |
3.07 |
1.48 |
0 |
5 |
5 |
-0.27 |
0.07 |
0.05 |
| pr07 |
7 |
513 |
2.60 |
1.12 |
3 |
2.63 |
1.48 |
0 |
5 |
5 |
-0.17 |
-0.10 |
0.05 |
| pr08 |
8 |
513 |
3.04 |
0.92 |
3 |
3.09 |
0.00 |
0 |
5 |
5 |
-0.55 |
1.25 |
0.04 |
| pr09 |
9 |
513 |
2.91 |
0.97 |
3 |
2.95 |
0.00 |
0 |
5 |
5 |
-0.48 |
1.00 |
0.04 |
| pr10 |
10 |
513 |
2.84 |
1.04 |
3 |
2.90 |
1.48 |
0 |
5 |
5 |
-0.41 |
0.31 |
0.05 |
taia %>%
select(all_of(co_items)) %>%
describe() %>%
kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)
Table 2: Consistency
| co01 |
1 |
513 |
2.51 |
1.09 |
3 |
2.53 |
1.48 |
0 |
5 |
5 |
-0.15 |
0.12 |
0.05 |
| co02 |
2 |
513 |
2.52 |
1.06 |
3 |
2.54 |
1.48 |
0 |
5 |
5 |
-0.18 |
-0.04 |
0.05 |
| co03 |
3 |
513 |
2.87 |
1.03 |
3 |
2.93 |
1.48 |
0 |
5 |
5 |
-0.38 |
0.33 |
0.05 |
| co04 |
4 |
513 |
3.46 |
1.10 |
4 |
3.53 |
1.48 |
0 |
5 |
5 |
-0.57 |
0.32 |
0.05 |
| co05 |
5 |
513 |
2.22 |
1.13 |
2 |
2.20 |
1.48 |
0 |
5 |
5 |
0.13 |
-0.17 |
0.05 |
| co06 |
6 |
513 |
2.52 |
1.12 |
3 |
2.53 |
1.48 |
0 |
5 |
5 |
-0.12 |
-0.15 |
0.05 |
| co07 |
7 |
513 |
1.60 |
1.14 |
2 |
1.54 |
1.48 |
0 |
5 |
5 |
0.53 |
0.09 |
0.05 |
| co08 |
8 |
513 |
1.93 |
1.07 |
2 |
1.88 |
1.48 |
0 |
5 |
5 |
0.42 |
0.22 |
0.05 |
| co09 |
9 |
513 |
2.08 |
1.09 |
2 |
2.03 |
1.48 |
0 |
5 |
5 |
0.34 |
0.09 |
0.05 |
| co10 |
10 |
513 |
2.46 |
1.11 |
2 |
2.46 |
1.48 |
0 |
5 |
5 |
-0.04 |
-0.07 |
0.05 |
taia %>%
select(all_of(ut_items)) %>%
describe() %>%
kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)
Table 3: Utility
| ut01 |
1 |
513 |
3.75 |
1.07 |
4 |
3.87 |
1.48 |
0 |
5 |
5 |
-0.87 |
1.08 |
0.05 |
| ut02 |
2 |
513 |
3.52 |
1.06 |
3 |
3.58 |
1.48 |
0 |
5 |
5 |
-0.54 |
0.54 |
0.05 |
| ut03 |
3 |
513 |
3.51 |
1.14 |
4 |
3.60 |
1.48 |
0 |
5 |
5 |
-0.57 |
0.06 |
0.05 |
| ut04 |
4 |
513 |
3.10 |
1.12 |
3 |
3.15 |
1.48 |
0 |
5 |
5 |
-0.43 |
0.04 |
0.05 |
| ut05 |
5 |
513 |
3.05 |
1.21 |
3 |
3.10 |
1.48 |
0 |
5 |
5 |
-0.34 |
-0.16 |
0.05 |
| ut06 |
6 |
513 |
3.27 |
1.10 |
3 |
3.31 |
1.48 |
0 |
5 |
5 |
-0.61 |
0.64 |
0.05 |
| ut07 |
7 |
513 |
3.20 |
1.13 |
3 |
3.22 |
1.48 |
0 |
5 |
5 |
-0.29 |
-0.19 |
0.05 |
| ut08 |
8 |
513 |
3.43 |
1.06 |
3 |
3.48 |
1.48 |
0 |
5 |
5 |
-0.57 |
0.47 |
0.05 |
| ut09 |
9 |
513 |
3.18 |
1.17 |
3 |
3.24 |
1.48 |
0 |
5 |
5 |
-0.48 |
0.17 |
0.05 |
| ut10 |
10 |
513 |
2.17 |
1.12 |
2 |
2.15 |
1.48 |
0 |
5 |
5 |
0.09 |
-0.22 |
0.05 |
| ut11 |
11 |
513 |
2.69 |
1.24 |
3 |
2.71 |
1.48 |
0 |
5 |
5 |
-0.13 |
-0.41 |
0.05 |
| ut12 |
12 |
513 |
3.16 |
1.15 |
3 |
3.21 |
1.48 |
0 |
5 |
5 |
-0.43 |
0.05 |
0.05 |
taia %>%
select(all_of(fa_items)) %>%
describe() %>%
kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)
Table 4: Faith
| fa01 |
1 |
513 |
2.44 |
1.11 |
2 |
2.44 |
1.48 |
0 |
5 |
5 |
-0.03 |
-0.27 |
0.05 |
| fa02 |
2 |
513 |
2.16 |
1.18 |
2 |
2.14 |
1.48 |
0 |
5 |
5 |
0.16 |
-0.42 |
0.05 |
| fa03 |
3 |
513 |
1.54 |
1.15 |
1 |
1.45 |
1.48 |
0 |
5 |
5 |
0.66 |
0.18 |
0.05 |
| fa04 |
4 |
513 |
1.61 |
1.10 |
2 |
1.55 |
1.48 |
0 |
5 |
5 |
0.54 |
0.12 |
0.05 |
| fa05 |
5 |
513 |
2.47 |
1.11 |
3 |
2.49 |
1.48 |
0 |
5 |
5 |
-0.13 |
-0.09 |
0.05 |
| fa06 |
6 |
513 |
2.49 |
1.08 |
3 |
2.50 |
1.48 |
0 |
5 |
5 |
-0.17 |
0.07 |
0.05 |
| fa07 |
7 |
513 |
2.36 |
1.08 |
2 |
2.36 |
1.48 |
0 |
5 |
5 |
-0.12 |
-0.15 |
0.05 |
| fa08 |
8 |
513 |
2.20 |
1.14 |
2 |
2.16 |
1.48 |
0 |
5 |
5 |
0.26 |
-0.12 |
0.05 |
| fa09 |
9 |
513 |
2.29 |
1.18 |
2 |
2.28 |
1.48 |
0 |
5 |
5 |
0.11 |
-0.41 |
0.05 |
| fa10 |
10 |
513 |
2.64 |
1.20 |
3 |
2.62 |
1.48 |
0 |
5 |
5 |
0.06 |
-0.31 |
0.05 |
taia %>%
select(all_of(de_items)) %>%
describe() %>%
kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)
Table 5: Dependability
| de01 |
1 |
513 |
2.60 |
1.11 |
3 |
2.65 |
1.48 |
0 |
5 |
5 |
-0.41 |
0.07 |
0.05 |
| de02 |
2 |
513 |
2.19 |
1.16 |
2 |
2.20 |
1.48 |
0 |
5 |
5 |
0.00 |
-0.33 |
0.05 |
| de03 |
3 |
513 |
2.19 |
1.21 |
2 |
2.20 |
1.48 |
0 |
5 |
5 |
0.02 |
-0.31 |
0.05 |
| de04 |
4 |
513 |
1.91 |
1.06 |
2 |
1.86 |
1.48 |
0 |
5 |
5 |
0.55 |
0.57 |
0.05 |
| de05 |
5 |
513 |
3.53 |
1.18 |
4 |
3.64 |
1.48 |
0 |
5 |
5 |
-0.78 |
0.46 |
0.05 |
| de06 |
6 |
513 |
2.23 |
1.23 |
2 |
2.25 |
1.48 |
0 |
5 |
5 |
0.00 |
-0.44 |
0.05 |
| de07 |
7 |
513 |
2.82 |
1.02 |
3 |
2.86 |
1.48 |
0 |
5 |
5 |
-0.31 |
0.32 |
0.04 |
| de08 |
8 |
513 |
2.64 |
1.05 |
3 |
2.69 |
1.48 |
0 |
5 |
5 |
-0.40 |
0.14 |
0.05 |
| de09 |
9 |
513 |
3.42 |
1.21 |
4 |
3.51 |
1.48 |
0 |
5 |
5 |
-0.57 |
-0.22 |
0.05 |
| de10 |
10 |
513 |
2.26 |
1.20 |
2 |
2.29 |
1.48 |
0 |
5 |
5 |
-0.21 |
-0.41 |
0.05 |
| de11 |
11 |
513 |
2.32 |
1.20 |
2 |
2.32 |
1.48 |
0 |
5 |
5 |
0.00 |
-0.52 |
0.05 |
taia %>%
select(all_of(un_items)) %>%
describe() %>%
kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)
Table 6: Understanding
| un01 |
1 |
513 |
2.95 |
1.06 |
3 |
3.02 |
1.48 |
0 |
5 |
5 |
-0.48 |
0.31 |
0.05 |
| un02 |
2 |
513 |
2.49 |
1.14 |
3 |
2.51 |
1.48 |
0 |
5 |
5 |
-0.19 |
-0.25 |
0.05 |
| un03 |
3 |
513 |
3.02 |
1.17 |
3 |
3.09 |
1.48 |
0 |
5 |
5 |
-0.54 |
0.02 |
0.05 |
| un04 |
4 |
513 |
2.62 |
1.09 |
3 |
2.67 |
1.48 |
0 |
5 |
5 |
-0.33 |
-0.18 |
0.05 |
| un05 |
5 |
513 |
2.83 |
1.11 |
3 |
2.90 |
1.48 |
0 |
5 |
5 |
-0.49 |
0.23 |
0.05 |
| un06 |
6 |
513 |
2.28 |
1.23 |
2 |
2.25 |
1.48 |
0 |
5 |
5 |
0.20 |
-0.60 |
0.05 |
| un07 |
7 |
513 |
2.16 |
1.19 |
2 |
2.17 |
1.48 |
0 |
5 |
5 |
-0.01 |
-0.52 |
0.05 |
| un08 |
8 |
513 |
2.90 |
1.16 |
3 |
2.96 |
1.48 |
0 |
5 |
5 |
-0.44 |
0.02 |
0.05 |
| un09 |
9 |
513 |
2.34 |
1.23 |
2 |
2.39 |
1.48 |
0 |
5 |
5 |
-0.19 |
-0.71 |
0.05 |
| un10 |
10 |
513 |
2.27 |
1.16 |
2 |
2.26 |
1.48 |
0 |
5 |
5 |
0.05 |
-0.44 |
0.05 |
| un11 |
11 |
513 |
2.63 |
1.20 |
3 |
2.67 |
1.48 |
0 |
5 |
5 |
-0.24 |
-0.36 |
0.05 |
| un12 |
12 |
513 |
2.89 |
1.13 |
3 |
2.95 |
1.48 |
0 |
5 |
5 |
-0.43 |
0.10 |
0.05 |
TAIA scores distributions
taia %>% select(all_of(pr_items)) %>%
pivot_longer(cols = pr_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkred") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Predictability") +
theme(plot.title = element_text(hjust = .5))

taia %>% select(all_of(co_items)) %>%
pivot_longer(cols = co_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "chocolate3") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Consistency") +
theme(plot.title = element_text(hjust = .5))

taia %>% select(all_of(ut_items)) %>%
pivot_longer(cols = ut_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "goldenrod3") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Utility") +
theme(plot.title = element_text(hjust = .5))

taia %>% select(all_of(fa_items)) %>%
pivot_longer(cols = fa_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkgreen") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Faith") +
theme(plot.title = element_text(hjust = .5))

taia %>% select(all_of(de_items)) %>%
pivot_longer(cols = de_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkblue") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Dependability") +
theme(plot.title = element_text(hjust = .5))

taia %>% select(all_of(un_items)) %>%
pivot_longer(cols = un_items) %>%
ggplot(aes(value)) +
geom_bar(fill = "purple4") +
facet_wrap(~ name) +
scale_x_discrete(limits = factor(0:5)) +
labs(x = "Score", y = "Number of observations",
title = "Understanding") +
theme(plot.title = element_text(hjust = .5))

Correlations
Predictability
corrplot.mixed(cor(taia %>% select(all_of(pr_items))),
lower.col = "black")

Consistency
corrplot.mixed(cor(taia %>% select(all_of(co_items))),
lower.col = "black")

Utility
corrplot.mixed(cor(taia %>% select(all_of(ut_items))),
lower.col = "black")

Faith
corrplot.mixed(cor(taia %>% select(all_of(fa_items))),
lower.col = "black")

Dependability
corrplot.mixed(cor(taia %>% select(all_of(de_items))),
lower.col = "black")

Understanding
corrplot.mixed(cor(taia %>% select(all_of(un_items))),
lower.col = "black")

All TAIA items correlations
qgraph::qgraph(
cor(taia %>% select(all_of(taia_items))),
layout = "spring",
posCol = "darkgreen",
negCol = "darkred"
)

Psychometric Analysis
Subscales
Predictability
pr1 <- psych::alpha(
taia %>% select(all_of(pr_items)),
cumulative = TRUE,
title = "Predictability Factor",
check.keys = FALSE
)
kable(pr1$total,
caption = "Perdictability. Subscale statistics",
label = 7, digits = 2,
col.names = total_colnames
)
Table 7: Perdictability. Subscale statistics
|
0.8 |
0.8 |
0.82 |
0.29 |
4.1 |
0.01 |
27.95 |
6.25 |
0.36 |
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
caption = "Predictability. Items statistics",
label = 8, digits = 2,
col.names = item_stats_colnames)
Table 8: Predictability. Items statistics
| pr01 |
513 |
0.77 |
0.78 |
0.78 |
0.70 |
0.57 |
1.00 |
| pr02 |
513 |
0.67 |
0.67 |
0.63 |
0.57 |
0.55 |
0.98 |
| pr03 |
513 |
0.39 |
0.39 |
0.29 |
0.23 |
0.57 |
1.04 |
| pr04 |
513 |
0.29 |
0.28 |
0.17 |
0.12 |
0.56 |
1.07 |
| pr05 |
513 |
0.61 |
0.59 |
0.52 |
0.47 |
0.45 |
1.21 |
| pr06 |
513 |
0.64 |
0.63 |
0.57 |
0.51 |
0.61 |
1.08 |
| pr07 |
513 |
0.74 |
0.73 |
0.71 |
0.64 |
0.52 |
1.12 |
| pr08 |
513 |
0.73 |
0.74 |
0.71 |
0.64 |
0.61 |
0.92 |
| pr09 |
513 |
0.62 |
0.63 |
0.57 |
0.51 |
0.58 |
0.97 |
| pr10 |
513 |
0.57 |
0.57 |
0.49 |
0.44 |
0.57 |
1.04 |
pr1$item.stats %>%
ggplot(aes(x = row.names(pr1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Predictability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(pr1$alpha.drop,
caption = "Predictability. Subscale statistics when item drop",
label = 9, digits = 2, col.names = alpha_drop)
Table 9: Predictability. Subscale statistics when item drop
| pr01 |
0.76 |
0.76 |
0.78 |
0.26 |
3.18 |
0.02 |
0.03 |
0.32 |
| pr02 |
0.77 |
0.78 |
0.79 |
0.28 |
3.49 |
0.01 |
0.04 |
0.32 |
| pr03 |
0.81 |
0.81 |
0.82 |
0.33 |
4.37 |
0.01 |
0.03 |
0.38 |
| pr04 |
0.82 |
0.83 |
0.82 |
0.34 |
4.72 |
0.01 |
0.02 |
0.38 |
| pr05 |
0.78 |
0.79 |
0.81 |
0.29 |
3.71 |
0.01 |
0.03 |
0.36 |
| pr06 |
0.78 |
0.78 |
0.80 |
0.29 |
3.60 |
0.01 |
0.03 |
0.34 |
| pr07 |
0.76 |
0.77 |
0.79 |
0.27 |
3.32 |
0.02 |
0.03 |
0.32 |
| pr08 |
0.77 |
0.77 |
0.79 |
0.27 |
3.30 |
0.02 |
0.03 |
0.32 |
| pr09 |
0.78 |
0.78 |
0.80 |
0.29 |
3.61 |
0.01 |
0.03 |
0.35 |
| pr10 |
0.79 |
0.79 |
0.81 |
0.30 |
3.79 |
0.01 |
0.03 |
0.37 |
kable(pr1$response.freq,
caption = "Predictability. Non missing response frequency for each item",
label = 10, digits = 2)
Table 10: Predictability. Non missing response frequency for each item
| pr01 |
0.02 |
0.06 |
0.24 |
0.45 |
0.19 |
0.04 |
0 |
| pr02 |
0.02 |
0.08 |
0.27 |
0.43 |
0.17 |
0.03 |
0 |
| pr03 |
0.02 |
0.07 |
0.27 |
0.39 |
0.21 |
0.05 |
0 |
| pr04 |
0.02 |
0.07 |
0.28 |
0.37 |
0.21 |
0.05 |
0 |
| pr05 |
0.09 |
0.16 |
0.32 |
0.29 |
0.10 |
0.04 |
0 |
| pr06 |
0.02 |
0.06 |
0.20 |
0.40 |
0.24 |
0.09 |
0 |
| pr07 |
0.04 |
0.12 |
0.28 |
0.38 |
0.15 |
0.04 |
0 |
| pr08 |
0.02 |
0.03 |
0.16 |
0.51 |
0.24 |
0.04 |
0 |
| pr09 |
0.02 |
0.05 |
0.19 |
0.52 |
0.18 |
0.04 |
0 |
| pr10 |
0.03 |
0.08 |
0.21 |
0.45 |
0.19 |
0.04 |
0 |
Consistency
co1 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)
Some items ( co07 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
kable(co1$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)
Table 11: Consistency. Subscale statistics
|
0.77 |
0.78 |
0.81 |
0.26 |
3.48 |
0.01 |
24.18 |
6.28 |
0.3 |
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)
Table 12: Consistency. Items statistics
| co01 |
513 |
0.75 |
0.75 |
0.73 |
0.66 |
0.50 |
1.09 |
| co02 |
513 |
0.69 |
0.70 |
0.67 |
0.59 |
0.50 |
1.06 |
| co03 |
513 |
0.56 |
0.57 |
0.49 |
0.43 |
0.57 |
1.03 |
| co04 |
513 |
0.42 |
0.42 |
0.33 |
0.26 |
0.69 |
1.10 |
| co05 |
513 |
0.79 |
0.79 |
0.79 |
0.71 |
0.44 |
1.13 |
| co06 |
513 |
0.66 |
0.66 |
0.62 |
0.54 |
0.50 |
1.12 |
| co07 |
513 |
-0.07 |
-0.08 |
-0.25 |
-0.25 |
0.32 |
1.14 |
| co08 |
513 |
0.49 |
0.49 |
0.40 |
0.34 |
0.39 |
1.07 |
| co09 |
513 |
0.78 |
0.78 |
0.78 |
0.69 |
0.42 |
1.09 |
| co10 |
513 |
0.69 |
0.69 |
0.64 |
0.58 |
0.49 |
1.11 |
co1$item.stats %>%
ggplot(aes(x = row.names(co1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(co1$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop)
Table 13: Consistency. Subscale statistics when item drop
| co01 |
0.72 |
0.73 |
0.77 |
0.23 |
2.68 |
0.02 |
0.07 |
0.28 |
| co02 |
0.74 |
0.74 |
0.78 |
0.24 |
2.82 |
0.02 |
0.07 |
0.29 |
| co03 |
0.76 |
0.76 |
0.80 |
0.26 |
3.15 |
0.02 |
0.07 |
0.29 |
| co04 |
0.78 |
0.78 |
0.81 |
0.28 |
3.55 |
0.01 |
0.07 |
0.37 |
| co05 |
0.72 |
0.72 |
0.76 |
0.22 |
2.59 |
0.02 |
0.06 |
0.28 |
| co06 |
0.74 |
0.74 |
0.79 |
0.24 |
2.91 |
0.02 |
0.07 |
0.29 |
| co07 |
0.84 |
0.84 |
0.85 |
0.36 |
5.13 |
0.01 |
0.02 |
0.39 |
| co08 |
0.77 |
0.77 |
0.80 |
0.27 |
3.36 |
0.01 |
0.07 |
0.35 |
| co09 |
0.72 |
0.72 |
0.76 |
0.23 |
2.62 |
0.02 |
0.07 |
0.28 |
| co10 |
0.74 |
0.74 |
0.78 |
0.24 |
2.84 |
0.02 |
0.07 |
0.28 |
kable(co1$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)
Table 14: Consistency. Non missing response frequency for each item
| co01 |
0.05 |
0.11 |
0.31 |
0.39 |
0.11 |
0.04 |
0 |
| co02 |
0.04 |
0.12 |
0.32 |
0.37 |
0.13 |
0.02 |
0 |
| co03 |
0.02 |
0.07 |
0.22 |
0.44 |
0.20 |
0.04 |
0 |
| co04 |
0.01 |
0.04 |
0.11 |
0.34 |
0.32 |
0.18 |
0 |
| co05 |
0.06 |
0.19 |
0.36 |
0.27 |
0.09 |
0.03 |
0 |
| co06 |
0.04 |
0.14 |
0.28 |
0.38 |
0.12 |
0.04 |
0 |
| co07 |
0.18 |
0.31 |
0.32 |
0.14 |
0.04 |
0.02 |
0 |
| co08 |
0.08 |
0.26 |
0.42 |
0.17 |
0.06 |
0.02 |
0 |
| co09 |
0.06 |
0.24 |
0.39 |
0.23 |
0.06 |
0.03 |
0 |
| co10 |
0.04 |
0.13 |
0.33 |
0.34 |
0.12 |
0.04 |
0 |
Utility
ut1 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)
Some items ( ut10 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
kable(ut1$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)
Table 15: Utility. Subscale statistics
|
0.86 |
0.86 |
0.88 |
0.34 |
6.21 |
0.01 |
38.03 |
8.52 |
0.39 |
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)
Table 16: Utility. Items statistics
| ut01 |
513 |
0.78 |
0.78 |
0.78 |
0.72 |
0.75 |
1.07 |
| ut02 |
513 |
0.83 |
0.83 |
0.84 |
0.78 |
0.70 |
1.06 |
| ut03 |
513 |
0.52 |
0.52 |
0.46 |
0.42 |
0.70 |
1.14 |
| ut04 |
513 |
0.53 |
0.53 |
0.46 |
0.43 |
0.62 |
1.12 |
| ut05 |
513 |
0.70 |
0.70 |
0.66 |
0.62 |
0.61 |
1.21 |
| ut06 |
513 |
0.77 |
0.77 |
0.76 |
0.71 |
0.65 |
1.10 |
| ut07 |
513 |
0.64 |
0.64 |
0.60 |
0.55 |
0.64 |
1.13 |
| ut08 |
513 |
0.67 |
0.67 |
0.64 |
0.59 |
0.69 |
1.06 |
| ut09 |
513 |
0.69 |
0.69 |
0.66 |
0.61 |
0.64 |
1.17 |
| ut10 |
513 |
0.13 |
0.14 |
0.02 |
0.00 |
0.43 |
1.12 |
| ut11 |
513 |
0.57 |
0.56 |
0.49 |
0.46 |
0.54 |
1.24 |
| ut12 |
513 |
0.72 |
0.72 |
0.69 |
0.65 |
0.63 |
1.15 |
ut1$item.stats %>%
ggplot(aes(x = row.names(ut1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(ut1$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop)
Table 17: Utility. Subscale statistics when item drop
| ut01 |
0.84 |
0.84 |
0.85 |
0.32 |
5.18 |
0.01 |
0.04 |
0.33 |
| ut02 |
0.83 |
0.83 |
0.85 |
0.31 |
5.02 |
0.01 |
0.03 |
0.33 |
| ut03 |
0.86 |
0.86 |
0.87 |
0.36 |
6.07 |
0.01 |
0.04 |
0.42 |
| ut04 |
0.86 |
0.86 |
0.87 |
0.35 |
6.04 |
0.01 |
0.04 |
0.42 |
| ut05 |
0.84 |
0.85 |
0.86 |
0.33 |
5.46 |
0.01 |
0.04 |
0.39 |
| ut06 |
0.84 |
0.84 |
0.86 |
0.32 |
5.22 |
0.01 |
0.04 |
0.33 |
| ut07 |
0.85 |
0.85 |
0.86 |
0.34 |
5.66 |
0.01 |
0.04 |
0.39 |
| ut08 |
0.85 |
0.85 |
0.86 |
0.33 |
5.54 |
0.01 |
0.04 |
0.39 |
| ut09 |
0.84 |
0.85 |
0.86 |
0.33 |
5.49 |
0.01 |
0.04 |
0.39 |
| ut10 |
0.88 |
0.88 |
0.89 |
0.41 |
7.61 |
0.01 |
0.02 |
0.42 |
| ut11 |
0.85 |
0.86 |
0.87 |
0.35 |
5.94 |
0.01 |
0.04 |
0.42 |
| ut12 |
0.84 |
0.84 |
0.86 |
0.33 |
5.38 |
0.01 |
0.04 |
0.37 |
kable(ut1$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)
Table 18: Utility. Non missing response frequency for each item
| ut01 |
0.02 |
0.02 |
0.06 |
0.29 |
0.34 |
0.27 |
0 |
| ut02 |
0.01 |
0.02 |
0.09 |
0.38 |
0.30 |
0.20 |
0 |
| ut03 |
0.01 |
0.04 |
0.10 |
0.32 |
0.30 |
0.22 |
0 |
| ut04 |
0.02 |
0.08 |
0.15 |
0.40 |
0.27 |
0.09 |
0 |
| ut05 |
0.03 |
0.06 |
0.21 |
0.34 |
0.23 |
0.12 |
0 |
| ut06 |
0.03 |
0.03 |
0.14 |
0.38 |
0.30 |
0.12 |
0 |
| ut07 |
0.02 |
0.05 |
0.19 |
0.35 |
0.27 |
0.13 |
0 |
| ut08 |
0.01 |
0.03 |
0.12 |
0.35 |
0.34 |
0.15 |
0 |
| ut09 |
0.03 |
0.05 |
0.15 |
0.38 |
0.26 |
0.13 |
0 |
| ut10 |
0.07 |
0.19 |
0.37 |
0.26 |
0.09 |
0.02 |
0 |
| ut11 |
0.05 |
0.12 |
0.27 |
0.31 |
0.19 |
0.07 |
0 |
| ut12 |
0.02 |
0.06 |
0.15 |
0.38 |
0.26 |
0.12 |
0 |
Faith
fa1 <- psych::alpha(
taia %>% select(all_of(fa_items)),
cumulative = TRUE,
title = "Faith Factor",
check.keys = FALSE
)
kable(fa1$total,
caption = "Faith. Subscale statistics",
label = 19, digits = 2,
col.names = total_colnames)
Table 19: Faith. Subscale statistics
|
0.76 |
0.76 |
0.81 |
0.24 |
3.12 |
0.02 |
22.18 |
6.36 |
0.25 |
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
caption = "Faith. Items statistics",
label = 20, digits = 2,
col.names = item_stats_colnames)
Table 20: Faith. Items statistics
| fa01 |
513 |
0.76 |
0.76 |
0.77 |
0.67 |
0.49 |
1.11 |
| fa02 |
513 |
0.59 |
0.58 |
0.54 |
0.45 |
0.43 |
1.18 |
| fa03 |
513 |
0.29 |
0.29 |
0.17 |
0.11 |
0.31 |
1.15 |
| fa04 |
513 |
0.42 |
0.43 |
0.34 |
0.26 |
0.32 |
1.10 |
| fa05 |
513 |
0.77 |
0.77 |
0.78 |
0.68 |
0.49 |
1.11 |
| fa06 |
513 |
0.55 |
0.57 |
0.51 |
0.42 |
0.50 |
1.08 |
| fa07 |
513 |
0.40 |
0.40 |
0.29 |
0.24 |
0.47 |
1.08 |
| fa08 |
513 |
0.56 |
0.56 |
0.50 |
0.42 |
0.44 |
1.14 |
| fa09 |
513 |
0.63 |
0.62 |
0.59 |
0.50 |
0.46 |
1.18 |
| fa10 |
513 |
0.63 |
0.62 |
0.57 |
0.50 |
0.53 |
1.20 |
fa1$item.stats %>%
ggplot(aes(x = row.names(fa1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Faith. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(fa1$alpha.drop,
caption = "Faith. Subscale statistics when item drop",
label = 21, digits = 2,
col.names = alpha_drop)
Table 21: Faith. Subscale statistics when item drop
| fa01 |
0.70 |
0.70 |
0.76 |
0.21 |
2.34 |
0.02 |
0.04 |
0.23 |
| fa02 |
0.73 |
0.73 |
0.78 |
0.23 |
2.76 |
0.02 |
0.04 |
0.25 |
| fa03 |
0.78 |
0.78 |
0.82 |
0.28 |
3.50 |
0.01 |
0.04 |
0.29 |
| fa04 |
0.76 |
0.76 |
0.80 |
0.26 |
3.13 |
0.02 |
0.05 |
0.25 |
| fa05 |
0.70 |
0.70 |
0.76 |
0.20 |
2.31 |
0.02 |
0.04 |
0.23 |
| fa06 |
0.74 |
0.74 |
0.79 |
0.24 |
2.79 |
0.02 |
0.05 |
0.27 |
| fa07 |
0.76 |
0.76 |
0.81 |
0.26 |
3.19 |
0.02 |
0.05 |
0.29 |
| fa08 |
0.74 |
0.74 |
0.79 |
0.24 |
2.82 |
0.02 |
0.04 |
0.25 |
| fa09 |
0.73 |
0.73 |
0.78 |
0.23 |
2.66 |
0.02 |
0.04 |
0.24 |
| fa10 |
0.73 |
0.73 |
0.79 |
0.23 |
2.65 |
0.02 |
0.05 |
0.24 |
kable(fa1$response.freq,
caption = "Faith. Non missing response frequency for each item",
label = 22, digits = 2)
Table 22: Faith. Non missing response frequency for each item
| fa01 |
0.04 |
0.16 |
0.31 |
0.34 |
0.12 |
0.03 |
0 |
| fa02 |
0.08 |
0.20 |
0.36 |
0.21 |
0.12 |
0.02 |
0 |
| fa03 |
0.19 |
0.34 |
0.30 |
0.11 |
0.05 |
0.02 |
0 |
| fa04 |
0.15 |
0.34 |
0.33 |
0.13 |
0.04 |
0.01 |
0 |
| fa05 |
0.05 |
0.12 |
0.33 |
0.34 |
0.13 |
0.03 |
0 |
| fa06 |
0.05 |
0.12 |
0.31 |
0.38 |
0.11 |
0.03 |
0 |
| fa07 |
0.05 |
0.15 |
0.33 |
0.34 |
0.11 |
0.02 |
0 |
| fa08 |
0.06 |
0.20 |
0.38 |
0.24 |
0.09 |
0.03 |
0 |
| fa09 |
0.06 |
0.19 |
0.34 |
0.26 |
0.13 |
0.03 |
0 |
| fa10 |
0.04 |
0.12 |
0.31 |
0.31 |
0.14 |
0.08 |
0 |
Dependability
de1 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)
Some items ( de04 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
kable(de1$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)
Table 23: Dependability. Subscale statistics
|
0.74 |
0.74 |
0.79 |
0.2 |
2.82 |
0.02 |
28.11 |
6.7 |
0.24 |
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)
Table 24: Dependability. Items statistics
| de01 |
513 |
0.58 |
0.59 |
0.53 |
0.45 |
0.52 |
1.11 |
| de02 |
513 |
0.72 |
0.72 |
0.70 |
0.62 |
0.44 |
1.16 |
| de03 |
513 |
0.66 |
0.65 |
0.61 |
0.53 |
0.44 |
1.21 |
| de04 |
513 |
-0.24 |
-0.24 |
-0.41 |
-0.38 |
0.38 |
1.06 |
| de05 |
513 |
0.58 |
0.57 |
0.55 |
0.44 |
0.71 |
1.18 |
| de06 |
513 |
0.67 |
0.66 |
0.62 |
0.55 |
0.45 |
1.23 |
| de07 |
513 |
0.58 |
0.60 |
0.54 |
0.47 |
0.56 |
1.02 |
| de08 |
513 |
0.67 |
0.68 |
0.65 |
0.57 |
0.53 |
1.05 |
| de09 |
513 |
0.44 |
0.43 |
0.37 |
0.27 |
0.68 |
1.21 |
| de10 |
513 |
0.73 |
0.73 |
0.72 |
0.63 |
0.45 |
1.20 |
| de11 |
513 |
0.40 |
0.39 |
0.27 |
0.23 |
0.46 |
1.20 |
de1$item.stats %>%
ggplot(aes(x = row.names(de1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(de1$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop)
Table 25: Dependability. Subscale statistics when item drop
| de01 |
0.72 |
0.71 |
0.77 |
0.20 |
2.44 |
0.02 |
0.07 |
0.21 |
| de02 |
0.69 |
0.69 |
0.75 |
0.18 |
2.19 |
0.02 |
0.07 |
0.21 |
| de03 |
0.70 |
0.70 |
0.76 |
0.19 |
2.31 |
0.02 |
0.07 |
0.21 |
| de04 |
0.81 |
0.81 |
0.83 |
0.30 |
4.33 |
0.01 |
0.02 |
0.31 |
| de05 |
0.72 |
0.71 |
0.75 |
0.20 |
2.47 |
0.02 |
0.07 |
0.20 |
| de06 |
0.70 |
0.70 |
0.76 |
0.19 |
2.30 |
0.02 |
0.07 |
0.21 |
| de07 |
0.72 |
0.71 |
0.77 |
0.19 |
2.42 |
0.02 |
0.07 |
0.21 |
| de08 |
0.70 |
0.69 |
0.76 |
0.18 |
2.26 |
0.02 |
0.06 |
0.21 |
| de09 |
0.74 |
0.73 |
0.77 |
0.22 |
2.77 |
0.02 |
0.07 |
0.29 |
| de10 |
0.69 |
0.68 |
0.75 |
0.18 |
2.16 |
0.02 |
0.07 |
0.21 |
| de11 |
0.75 |
0.74 |
0.80 |
0.22 |
2.85 |
0.02 |
0.08 |
0.31 |
kable(de1$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)
Table 26: Dependability. Non missing response frequency for each item
| de01 |
0.05 |
0.10 |
0.25 |
0.42 |
0.15 |
0.03 |
0 |
| de02 |
0.09 |
0.17 |
0.36 |
0.26 |
0.11 |
0.02 |
0 |
| de03 |
0.10 |
0.16 |
0.34 |
0.28 |
0.09 |
0.03 |
0 |
| de04 |
0.07 |
0.26 |
0.44 |
0.14 |
0.05 |
0.02 |
0 |
| de05 |
0.02 |
0.04 |
0.10 |
0.28 |
0.33 |
0.22 |
0 |
| de06 |
0.10 |
0.17 |
0.32 |
0.28 |
0.11 |
0.03 |
0 |
| de07 |
0.03 |
0.05 |
0.27 |
0.41 |
0.20 |
0.04 |
0 |
| de08 |
0.04 |
0.10 |
0.25 |
0.44 |
0.15 |
0.03 |
0 |
| de09 |
0.01 |
0.06 |
0.13 |
0.27 |
0.32 |
0.20 |
0 |
| de10 |
0.10 |
0.14 |
0.29 |
0.34 |
0.10 |
0.02 |
0 |
| de11 |
0.07 |
0.20 |
0.28 |
0.30 |
0.12 |
0.03 |
0 |
Understanding
un1 <- psych::alpha(
taia %>% select(all_of(un_items)),
cumulative = TRUE,
title = "Understanding Factor",
check.keys = FALSE
)
kable(un1$total,
caption = "Understanding. Subscale statistics",
label = 27, digits = 2,
col.names = total_colnames)
Table 27: Understanding. Subscale statistics
|
0.92 |
0.92 |
0.92 |
0.5 |
12.04 |
0.01 |
31.37 |
10.2 |
0.52 |
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
caption = "Understanding. Items statistics",
label = 28, digits = 2,
col.names = item_stats_colnames)
Table 28: Understanding. Items statistics
| un01 |
513 |
0.76 |
0.76 |
0.74 |
0.71 |
0.59 |
1.06 |
| un02 |
513 |
0.84 |
0.84 |
0.84 |
0.80 |
0.50 |
1.14 |
| un03 |
513 |
0.60 |
0.60 |
0.54 |
0.52 |
0.60 |
1.17 |
| un04 |
513 |
0.77 |
0.77 |
0.75 |
0.72 |
0.52 |
1.09 |
| un05 |
513 |
0.81 |
0.81 |
0.80 |
0.77 |
0.57 |
1.11 |
| un06 |
513 |
0.51 |
0.51 |
0.44 |
0.42 |
0.46 |
1.23 |
| un07 |
513 |
0.73 |
0.73 |
0.70 |
0.67 |
0.43 |
1.19 |
| un08 |
513 |
0.77 |
0.77 |
0.75 |
0.72 |
0.58 |
1.16 |
| un09 |
513 |
0.74 |
0.73 |
0.70 |
0.68 |
0.47 |
1.23 |
| un10 |
513 |
0.75 |
0.75 |
0.73 |
0.70 |
0.45 |
1.16 |
| un11 |
513 |
0.79 |
0.79 |
0.77 |
0.74 |
0.53 |
1.20 |
| un12 |
513 |
0.76 |
0.76 |
0.74 |
0.70 |
0.58 |
1.13 |
un1$item.stats %>%
ggplot(aes(x = row.names(un1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.05, color = "darkblue") +
geom_hline(yintercept = 0.95, color = "darkblue") +
geom_hline(yintercept = 0.2, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Understanding. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))

kable(un1$alpha.drop,
caption = "Understanding. Subscale statistics when item drop",
label = 29, digits = 2,
col.names = alpha_drop)
Table 29: Understanding. Subscale statistics when item drop
| un01 |
0.91 |
0.92 |
0.92 |
0.50 |
10.86 |
0.01 |
0.01 |
0.52 |
| un02 |
0.91 |
0.91 |
0.91 |
0.48 |
10.31 |
0.01 |
0.01 |
0.51 |
| un03 |
0.92 |
0.92 |
0.92 |
0.52 |
12.04 |
0.01 |
0.01 |
0.55 |
| un04 |
0.91 |
0.92 |
0.92 |
0.50 |
10.80 |
0.01 |
0.01 |
0.52 |
| un05 |
0.91 |
0.91 |
0.91 |
0.49 |
10.50 |
0.01 |
0.01 |
0.52 |
| un06 |
0.93 |
0.93 |
0.93 |
0.54 |
12.78 |
0.00 |
0.01 |
0.55 |
| un07 |
0.92 |
0.92 |
0.92 |
0.50 |
11.09 |
0.01 |
0.01 |
0.52 |
| un08 |
0.91 |
0.92 |
0.91 |
0.50 |
10.79 |
0.01 |
0.01 |
0.52 |
| un09 |
0.92 |
0.92 |
0.92 |
0.50 |
11.05 |
0.01 |
0.01 |
0.52 |
| un10 |
0.91 |
0.92 |
0.92 |
0.50 |
10.92 |
0.01 |
0.01 |
0.52 |
| un11 |
0.91 |
0.91 |
0.91 |
0.49 |
10.65 |
0.01 |
0.01 |
0.51 |
| un12 |
0.91 |
0.92 |
0.92 |
0.50 |
10.86 |
0.01 |
0.01 |
0.52 |
kable(un1$response.freq,
caption = "Understanding. Non missing response frequency for each item",
label = 30, digits = 2)
Table 30: Understanding. Non missing response frequency for each item
| un01 |
0.03 |
0.07 |
0.19 |
0.42 |
0.24 |
0.05 |
0 |
| un02 |
0.05 |
0.13 |
0.29 |
0.35 |
0.14 |
0.03 |
0 |
| un03 |
0.03 |
0.08 |
0.16 |
0.36 |
0.28 |
0.08 |
0 |
| un04 |
0.04 |
0.13 |
0.24 |
0.41 |
0.17 |
0.03 |
0 |
| un05 |
0.04 |
0.08 |
0.19 |
0.43 |
0.21 |
0.05 |
0 |
| un06 |
0.06 |
0.24 |
0.29 |
0.25 |
0.13 |
0.04 |
0 |
| un07 |
0.09 |
0.20 |
0.30 |
0.29 |
0.10 |
0.02 |
0 |
| un08 |
0.04 |
0.08 |
0.19 |
0.40 |
0.23 |
0.07 |
0 |
| un09 |
0.08 |
0.18 |
0.25 |
0.31 |
0.16 |
0.02 |
0 |
| un10 |
0.06 |
0.19 |
0.33 |
0.27 |
0.12 |
0.02 |
0 |
| un11 |
0.05 |
0.13 |
0.24 |
0.35 |
0.18 |
0.05 |
0 |
| un12 |
0.04 |
0.08 |
0.20 |
0.40 |
0.23 |
0.06 |
0 |
Items exclusion
Exploratory Factor Analysis
6 factors, varimax rotation
efa_6f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "varimax")
loadings(efa_6f_vm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
pr01 0.511 0.438 0.167 0.268
pr02 0.330 0.376 0.233 0.173
pr03 0.177 0.110 0.612
pr04 -0.150 0.134 0.440
pr05 0.227 0.384 0.156 0.108 0.653
pr06 0.518 0.260 0.150 0.113
pr07 0.417 0.511 0.193 0.126
pr08 0.550 0.383 0.135 0.192
pr09 0.406 0.322 0.150 0.136
pr10 0.376 0.284 0.137
co01 0.283 0.663
co02 0.229 0.614 0.120
co03 0.465 0.344 0.119 0.138
co04 0.577 0.110 0.127 0.103
co05 0.190 0.705 -0.109
co06 0.217 0.531 -0.140
co07 -0.357 -0.104 -0.101 0.117 0.128
co08 0.450 -0.145 -0.454
co09 0.132 0.676 -0.174
co10 0.228 0.518 0.211 -0.152
ut01 0.810
ut02 0.803 0.105 0.112 0.140
ut03 0.419 -0.154 0.128 0.570
ut04 0.485 0.114 0.145
ut05 0.606 0.181 0.105
ut06 0.731 0.139 0.133
ut07 0.565 0.188
ut08 0.609 0.212
ut09 0.613 0.151 0.148
ut10 -0.118 0.259
ut11 0.392 0.267 0.174 0.577
ut12 0.625 0.193 0.137 0.125
fa01 0.251 0.368 0.640
fa02 -0.192 0.628 0.111
fa03 -0.101 0.438 -0.306 0.115
fa04 0.582 -0.133 0.123
fa05 0.251 0.429 0.621
fa06 0.281 0.580 0.111 0.207 0.221
fa07 0.174 0.489 0.182
fa08 -0.180 0.580 0.190 0.120
fa09 -0.130 0.589 0.247 0.110
fa10 0.251 0.134 0.611
de01 0.303 0.422 0.158
de02 0.261 0.570 0.138 0.173 0.191
de03 0.216 0.500 0.117 0.149 0.186
de04 -0.462 -0.154 -0.147
de05 0.558 0.118 0.132 0.329
de06 0.218 0.361 0.181 0.190 0.135 0.610
de07 0.463 0.285 0.216 0.110 0.149
de08 0.365 0.389 0.184 0.123 0.201 0.182
de09 0.285 0.558
de10 0.287 0.508 0.176 0.110 0.312
de11 0.179 0.351 0.236
un01 0.316 0.727
un02 0.148 0.807
un03 0.235 0.501 -0.241
un04 0.137 0.125 0.712
un05 0.225 0.783
un06 -0.151 0.488 0.243
un07 0.310 0.646 -0.140 0.115
un08 0.227 0.744
un09 0.182 0.664 0.181
un10 0.277 0.690 -0.114 -0.100
un11 0.757
un12 0.277 0.709
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
SS loadings 8.708 7.011 6.516 2.794 2.782 1.803
Proportion Var 0.134 0.108 0.100 0.043 0.043 0.028
Cumulative Var 0.134 0.242 0.342 0.385 0.428 0.456
kable(sort(efa_6f_vm$uniquenesses, decreasing = TRUE), col.names = "U")
| ut10 |
0.9073192 |
| co07 |
0.8183857 |
| de11 |
0.7822050 |
| pr04 |
0.7591429 |
| pr10 |
0.7435298 |
| de04 |
0.7386776 |
| ut04 |
0.7276062 |
| de01 |
0.6927754 |
| fa07 |
0.6896196 |
| fa03 |
0.6893869 |
| pr09 |
0.6867794 |
| un06 |
0.6754405 |
| pr02 |
0.6569559 |
| ut07 |
0.6412519 |
| co06 |
0.6409573 |
| un03 |
0.6324479 |
| de03 |
0.6312699 |
| pr06 |
0.6270480 |
| de07 |
0.6223023 |
| co04 |
0.6192165 |
| co03 |
0.6178353 |
| fa04 |
0.6107644 |
| co10 |
0.6080403 |
| de09 |
0.6057377 |
| de08 |
0.5934608 |
| ut05 |
0.5796609 |
| pr03 |
0.5755150 |
| ut08 |
0.5742453 |
| fa08 |
0.5702292 |
| ut09 |
0.5690733 |
| fa09 |
0.5635590 |
| co08 |
0.5522230 |
| de05 |
0.5493456 |
| co02 |
0.5476391 |
| fa10 |
0.5387218 |
| fa02 |
0.5357916 |
| ut12 |
0.5316183 |
| de10 |
0.5162880 |
| de02 |
0.5152999 |
| pr07 |
0.4950670 |
| pr08 |
0.4938099 |
| co09 |
0.4888758 |
| un09 |
0.4762922 |
| fa06 |
0.4723240 |
| co01 |
0.4635556 |
| ut03 |
0.4558692 |
| co05 |
0.4538278 |
| un04 |
0.4458240 |
| un07 |
0.4442298 |
| pr01 |
0.4373561 |
| ut06 |
0.4224545 |
| un10 |
0.4212091 |
| un12 |
0.4164551 |
| ut11 |
0.3975327 |
| un11 |
0.3965605 |
| un08 |
0.3900697 |
| fa01 |
0.3826694 |
| un01 |
0.3632078 |
| de06 |
0.3629276 |
| fa05 |
0.3610612 |
| ut01 |
0.3310036 |
| pr05 |
0.3296280 |
| un05 |
0.3281390 |
| un02 |
0.3133867 |
| ut02 |
0.3056410 |
6 factors, promax rotation
efa_6f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "promax")
loadings(efa_6f_pm)
Loadings:
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
pr01 0.433 0.255 0.272
pr02 0.303 0.156 0.127 0.147
pr03 -0.117 0.688
pr04 -0.170 0.483
pr05 0.108 0.120 0.747
pr06 0.150 0.468
pr07 0.513 0.170 0.173
pr08 0.362 0.367 0.192
pr09 0.307 0.242 0.131
pr10 0.232 0.276 -0.106
co01 0.777 -0.113
co02 0.729 -0.124
co03 0.268 0.342 0.103
co04 0.557 -0.116
co05 0.848 -0.100 -0.107
co06 0.629 -0.164
co07 -0.105 -0.361 0.173
co08 0.560 -0.174 -0.487
co09 0.793 -0.112 -0.190
co10 0.567 0.113 -0.158
ut01 -0.260 0.957
ut02 -0.236 0.905 0.137
ut03 -0.204 0.330 -0.103 0.613
ut04 -0.199 0.596 -0.113 0.162
ut05 0.645
ut06 0.803 0.124
ut07 0.113 0.587
ut08 0.140 0.583
ut09 0.643 0.144
ut10 0.127 -0.199 -0.168 0.307 -0.102
ut11 0.357 0.664
ut12 0.585
fa01 0.345 0.115 0.674
fa02 -0.119 0.642
fa03 0.487 -0.183 -0.364 0.115
fa04 0.680 -0.308 -0.166
fa05 0.437 0.657
fa06 0.639 0.211 0.154
fa07 -0.141 0.496 0.170
fa08 -0.111 -0.117 0.568 0.112
fa09 0.159 0.570
fa10 0.228 -0.104 0.673 -0.136
de01 0.455 0.101
de02 0.585 0.152 0.140
de03 0.482 0.110 0.150
de04 -0.429
de05 0.438 0.355
de06 0.688
de07 0.172 0.348 0.105 0.123
de08 0.321 0.151 0.161 0.142
de09 0.642 -0.106
de10 0.433 0.300
de11 -0.101 0.319 0.250
un01 -0.160 0.219 0.805 0.138 -0.112
un02 -0.118 0.860
un03 0.141 0.458 -0.237
un04 0.743
un05 0.845
un06 -0.387 0.539 0.320
un07 0.223 -0.138 0.644 -0.154 0.101
un08 -0.151 0.118 0.824 0.123
un09 0.658 0.191
un10 0.225 -0.214 0.703
un11 0.804
un12 -0.195 0.187 0.746
Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
SS loadings 7.517 7.235 6.633 3.061 2.736 2.155
Proportion Var 0.116 0.111 0.102 0.047 0.042 0.033
Cumulative Var 0.116 0.227 0.329 0.376 0.418 0.451
kable(sort(efa_6f_pm$uniquenesses, decreasing = TRUE), col.names = "U")
| ut10 |
0.9073192 |
| co07 |
0.8183857 |
| de11 |
0.7822050 |
| pr04 |
0.7591429 |
| pr10 |
0.7435298 |
| de04 |
0.7386776 |
| ut04 |
0.7276062 |
| de01 |
0.6927754 |
| fa07 |
0.6896196 |
| fa03 |
0.6893869 |
| pr09 |
0.6867794 |
| un06 |
0.6754405 |
| pr02 |
0.6569559 |
| ut07 |
0.6412519 |
| co06 |
0.6409573 |
| un03 |
0.6324479 |
| de03 |
0.6312699 |
| pr06 |
0.6270480 |
| de07 |
0.6223023 |
| co04 |
0.6192165 |
| co03 |
0.6178353 |
| fa04 |
0.6107644 |
| co10 |
0.6080403 |
| de09 |
0.6057377 |
| de08 |
0.5934608 |
| ut05 |
0.5796609 |
| pr03 |
0.5755150 |
| ut08 |
0.5742453 |
| fa08 |
0.5702292 |
| ut09 |
0.5690733 |
| fa09 |
0.5635590 |
| co08 |
0.5522230 |
| de05 |
0.5493456 |
| co02 |
0.5476391 |
| fa10 |
0.5387218 |
| fa02 |
0.5357916 |
| ut12 |
0.5316183 |
| de10 |
0.5162880 |
| de02 |
0.5152999 |
| pr07 |
0.4950670 |
| pr08 |
0.4938099 |
| co09 |
0.4888758 |
| un09 |
0.4762922 |
| fa06 |
0.4723240 |
| co01 |
0.4635556 |
| ut03 |
0.4558692 |
| co05 |
0.4538278 |
| un04 |
0.4458240 |
| un07 |
0.4442298 |
| pr01 |
0.4373561 |
| ut06 |
0.4224545 |
| un10 |
0.4212091 |
| un12 |
0.4164551 |
| ut11 |
0.3975327 |
| un11 |
0.3965605 |
| un08 |
0.3900697 |
| fa01 |
0.3826694 |
| un01 |
0.3632078 |
| de06 |
0.3629276 |
| fa05 |
0.3610612 |
| ut01 |
0.3310036 |
| pr05 |
0.3296280 |
| un05 |
0.3281390 |
| un02 |
0.3133867 |
| ut02 |
0.3056410 |
Confirmatory Factor Analysis
Model:
mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co07 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut10 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de04 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"
CFA model fitting:
model1 <- cfa(mdl1, taia %>% select(all_of(taia_items)))
Fit measures:
kable(
tibble(Measure = c("Chi-Squared", "DF", "p", "GFI", "AGFI", "CFI", "TLI", "SRMR", "RMSEA"),
Value = round(
fitmeasures(model1, c("chisq", "df", "pvalue", "gfi", "agfi", "cfi", "tli", "srmr", "rmsea")), 4)
)
)
| Chi-Squared |
7107.8205 |
| DF |
2000.0000 |
| p |
0.0000 |
| GFI |
0.6218 |
| AGFI |
0.5943 |
| CFI |
0.6975 |
| TLI |
0.6854 |
| SRMR |
0.0997 |
| RMSEA |
0.0706 |
Standardized solution:
smodel1 <- standardizedsolution(model1)
Loadings:
kable(
smodel1 %>%
filter(op == "=~") %>%
mutate_at(vars(4:9), function(x) round(x, 3)),
col.names = c("Factor", "", "Item", "Loading", "SE", "z", "p", "CI lower bound", "CI upper bound")
)
| PR |
=~ |
pr01 |
0.767 |
0.021 |
37.025 |
0.000 |
0.727 |
0.808 |
| PR |
=~ |
pr02 |
0.602 |
0.030 |
19.788 |
0.000 |
0.542 |
0.661 |
| PR |
=~ |
pr03 |
0.207 |
0.044 |
4.682 |
0.000 |
0.121 |
0.294 |
| PR |
=~ |
pr04 |
0.062 |
0.046 |
1.349 |
0.177 |
-0.028 |
0.152 |
| PR |
=~ |
pr05 |
0.575 |
0.032 |
18.138 |
0.000 |
0.513 |
0.638 |
| PR |
=~ |
pr06 |
0.606 |
0.030 |
20.070 |
0.000 |
0.547 |
0.665 |
| PR |
=~ |
pr07 |
0.729 |
0.023 |
31.541 |
0.000 |
0.684 |
0.774 |
| PR |
=~ |
pr08 |
0.730 |
0.023 |
31.634 |
0.000 |
0.685 |
0.775 |
| PR |
=~ |
pr09 |
0.598 |
0.031 |
19.577 |
0.000 |
0.538 |
0.658 |
| PR |
=~ |
pr10 |
0.520 |
0.034 |
15.114 |
0.000 |
0.452 |
0.587 |
| CO |
=~ |
co01 |
0.743 |
0.023 |
32.138 |
0.000 |
0.698 |
0.789 |
| CO |
=~ |
co02 |
0.702 |
0.026 |
27.452 |
0.000 |
0.652 |
0.753 |
| CO |
=~ |
co03 |
0.523 |
0.035 |
14.936 |
0.000 |
0.454 |
0.591 |
| CO |
=~ |
co04 |
0.368 |
0.041 |
8.982 |
0.000 |
0.288 |
0.448 |
| CO |
=~ |
co05 |
0.779 |
0.021 |
37.209 |
0.000 |
0.738 |
0.820 |
| CO |
=~ |
co06 |
0.628 |
0.030 |
21.072 |
0.000 |
0.569 |
0.686 |
| CO |
=~ |
co07 |
-0.248 |
0.044 |
-5.620 |
0.000 |
-0.335 |
-0.162 |
| CO |
=~ |
co08 |
0.373 |
0.041 |
9.152 |
0.000 |
0.293 |
0.453 |
| CO |
=~ |
co09 |
0.738 |
0.023 |
31.430 |
0.000 |
0.692 |
0.784 |
| CO |
=~ |
co10 |
0.626 |
0.030 |
20.974 |
0.000 |
0.568 |
0.685 |
| UT |
=~ |
ut01 |
0.774 |
0.020 |
38.677 |
0.000 |
0.735 |
0.814 |
| UT |
=~ |
ut02 |
0.838 |
0.016 |
52.962 |
0.000 |
0.807 |
0.869 |
| UT |
=~ |
ut03 |
0.431 |
0.038 |
11.352 |
0.000 |
0.356 |
0.505 |
| UT |
=~ |
ut04 |
0.489 |
0.036 |
13.755 |
0.000 |
0.420 |
0.559 |
| UT |
=~ |
ut05 |
0.662 |
0.027 |
24.620 |
0.000 |
0.610 |
0.715 |
| UT |
=~ |
ut06 |
0.762 |
0.021 |
36.557 |
0.000 |
0.721 |
0.803 |
| UT |
=~ |
ut07 |
0.583 |
0.031 |
18.659 |
0.000 |
0.521 |
0.644 |
| UT |
=~ |
ut08 |
0.645 |
0.028 |
23.086 |
0.000 |
0.590 |
0.699 |
| UT |
=~ |
ut09 |
0.672 |
0.026 |
25.455 |
0.000 |
0.620 |
0.723 |
| UT |
=~ |
ut10 |
-0.016 |
0.046 |
-0.338 |
0.735 |
-0.106 |
0.075 |
| UT |
=~ |
ut11 |
0.532 |
0.034 |
15.802 |
0.000 |
0.466 |
0.598 |
| UT |
=~ |
ut12 |
0.702 |
0.025 |
28.542 |
0.000 |
0.654 |
0.750 |
| FA |
=~ |
fa01 |
0.826 |
0.019 |
43.688 |
0.000 |
0.789 |
0.863 |
| FA |
=~ |
fa02 |
0.360 |
0.041 |
8.678 |
0.000 |
0.278 |
0.441 |
| FA |
=~ |
fa03 |
0.207 |
0.045 |
4.581 |
0.000 |
0.118 |
0.296 |
| FA |
=~ |
fa04 |
0.380 |
0.041 |
9.330 |
0.000 |
0.301 |
0.460 |
| FA |
=~ |
fa05 |
0.850 |
0.018 |
48.152 |
0.000 |
0.815 |
0.884 |
| FA |
=~ |
fa06 |
0.619 |
0.031 |
20.287 |
0.000 |
0.560 |
0.679 |
| FA |
=~ |
fa07 |
0.238 |
0.045 |
5.325 |
0.000 |
0.150 |
0.325 |
| FA |
=~ |
fa08 |
0.343 |
0.042 |
8.168 |
0.000 |
0.260 |
0.425 |
| FA |
=~ |
fa09 |
0.420 |
0.039 |
10.650 |
0.000 |
0.342 |
0.497 |
| FA |
=~ |
fa10 |
0.589 |
0.032 |
18.368 |
0.000 |
0.526 |
0.652 |
| DE |
=~ |
de01 |
0.578 |
0.032 |
18.128 |
0.000 |
0.515 |
0.640 |
| DE |
=~ |
de02 |
0.701 |
0.025 |
27.887 |
0.000 |
0.652 |
0.750 |
| DE |
=~ |
de03 |
0.601 |
0.031 |
19.538 |
0.000 |
0.540 |
0.661 |
| DE |
=~ |
de04 |
-0.455 |
0.037 |
-12.178 |
0.000 |
-0.529 |
-0.382 |
| DE |
=~ |
de05 |
0.522 |
0.035 |
15.095 |
0.000 |
0.454 |
0.590 |
| DE |
=~ |
de06 |
0.622 |
0.030 |
20.974 |
0.000 |
0.563 |
0.680 |
| DE |
=~ |
de07 |
0.618 |
0.030 |
20.741 |
0.000 |
0.560 |
0.677 |
| DE |
=~ |
de08 |
0.690 |
0.026 |
26.769 |
0.000 |
0.640 |
0.741 |
| DE |
=~ |
de09 |
0.280 |
0.043 |
6.509 |
0.000 |
0.196 |
0.364 |
| DE |
=~ |
de10 |
0.699 |
0.025 |
27.660 |
0.000 |
0.649 |
0.748 |
| DE |
=~ |
de11 |
0.198 |
0.045 |
4.437 |
0.000 |
0.111 |
0.286 |
| UN |
=~ |
un01 |
0.750 |
0.021 |
35.714 |
0.000 |
0.709 |
0.791 |
| UN |
=~ |
un02 |
0.825 |
0.016 |
51.916 |
0.000 |
0.794 |
0.857 |
| UN |
=~ |
un03 |
0.552 |
0.032 |
17.112 |
0.000 |
0.489 |
0.615 |
| UN |
=~ |
un04 |
0.746 |
0.021 |
35.122 |
0.000 |
0.705 |
0.788 |
| UN |
=~ |
un05 |
0.807 |
0.017 |
46.861 |
0.000 |
0.773 |
0.840 |
| UN |
=~ |
un06 |
0.432 |
0.037 |
11.522 |
0.000 |
0.358 |
0.505 |
| UN |
=~ |
un07 |
0.697 |
0.024 |
28.655 |
0.000 |
0.649 |
0.745 |
| UN |
=~ |
un08 |
0.760 |
0.020 |
37.374 |
0.000 |
0.720 |
0.800 |
| UN |
=~ |
un09 |
0.700 |
0.024 |
28.953 |
0.000 |
0.652 |
0.747 |
| UN |
=~ |
un10 |
0.719 |
0.023 |
31.319 |
0.000 |
0.674 |
0.764 |
| UN |
=~ |
un11 |
0.773 |
0.019 |
39.708 |
0.000 |
0.735 |
0.811 |
| UN |
=~ |
un12 |
0.744 |
0.021 |
34.839 |
0.000 |
0.702 |
0.786 |
Covariances:
kable(
smodel1 %>%
filter(op == "~~" & lhs != rhs) %>%
mutate_at(vars(4:9), function(x) round(x, 3)),
col.names = c("Factor", "", "Factor", "Covariance", "SE", "z", "p", "CI lower bound", "CI upper bound")
)
| PR |
~~ |
CO |
0.701 |
0.030 |
23.747 |
0.00 |
0.643 |
0.759 |
| PR |
~~ |
UT |
0.770 |
0.024 |
31.846 |
0.00 |
0.722 |
0.817 |
| PR |
~~ |
FA |
0.549 |
0.038 |
14.491 |
0.00 |
0.475 |
0.623 |
| PR |
~~ |
DE |
0.894 |
0.018 |
50.899 |
0.00 |
0.859 |
0.928 |
| PR |
~~ |
UN |
0.421 |
0.041 |
10.162 |
0.00 |
0.340 |
0.502 |
| CO |
~~ |
UT |
0.469 |
0.040 |
11.769 |
0.00 |
0.391 |
0.547 |
| CO |
~~ |
FA |
0.480 |
0.041 |
11.816 |
0.00 |
0.400 |
0.559 |
| CO |
~~ |
DE |
0.659 |
0.032 |
20.450 |
0.00 |
0.596 |
0.722 |
| CO |
~~ |
UN |
0.328 |
0.044 |
7.444 |
0.00 |
0.242 |
0.415 |
| UT |
~~ |
FA |
0.412 |
0.042 |
9.755 |
0.00 |
0.329 |
0.495 |
| UT |
~~ |
DE |
0.699 |
0.029 |
24.200 |
0.00 |
0.642 |
0.755 |
| UT |
~~ |
UN |
0.333 |
0.043 |
7.734 |
0.00 |
0.248 |
0.417 |
| FA |
~~ |
DE |
0.640 |
0.034 |
19.071 |
0.00 |
0.574 |
0.705 |
| FA |
~~ |
UN |
0.088 |
0.049 |
1.815 |
0.07 |
-0.007 |
0.184 |
| DE |
~~ |
UN |
0.409 |
0.042 |
9.717 |
0.00 |
0.326 |
0.491 |
Residuals:
kable(
smodel1 %>%
filter(op == "~~" & lhs == rhs) %>%
mutate_at(vars(4:9), function(x) round(x, 3)) %>%
select(-(2:3)),
col.names = c("Item", "Residual", "SE", "z", "p", "CI lower bound", "CI upper bound")
)
| pr01 |
0.411 |
0.032 |
12.942 |
0 |
0.349 |
0.474 |
| pr02 |
0.638 |
0.037 |
17.449 |
0 |
0.566 |
0.710 |
| pr03 |
0.957 |
0.018 |
52.044 |
0 |
0.921 |
0.993 |
| pr04 |
0.996 |
0.006 |
174.276 |
0 |
0.985 |
1.007 |
| pr05 |
0.669 |
0.037 |
18.314 |
0 |
0.597 |
0.740 |
| pr06 |
0.633 |
0.037 |
17.317 |
0 |
0.561 |
0.705 |
| pr07 |
0.468 |
0.034 |
13.900 |
0 |
0.402 |
0.535 |
| pr08 |
0.467 |
0.034 |
13.882 |
0 |
0.401 |
0.533 |
| pr09 |
0.642 |
0.037 |
17.551 |
0 |
0.570 |
0.714 |
| pr10 |
0.730 |
0.036 |
20.422 |
0 |
0.660 |
0.800 |
| co01 |
0.447 |
0.034 |
13.002 |
0 |
0.380 |
0.515 |
| co02 |
0.507 |
0.036 |
14.092 |
0 |
0.436 |
0.577 |
| co03 |
0.727 |
0.037 |
19.882 |
0 |
0.655 |
0.799 |
| co04 |
0.865 |
0.030 |
28.690 |
0 |
0.806 |
0.924 |
| co05 |
0.393 |
0.033 |
12.037 |
0 |
0.329 |
0.457 |
| co06 |
0.606 |
0.037 |
16.203 |
0 |
0.533 |
0.679 |
| co07 |
0.938 |
0.022 |
42.790 |
0 |
0.895 |
0.981 |
| co08 |
0.861 |
0.030 |
28.267 |
0 |
0.801 |
0.920 |
| co09 |
0.456 |
0.035 |
13.151 |
0 |
0.388 |
0.523 |
| co10 |
0.608 |
0.037 |
16.245 |
0 |
0.534 |
0.681 |
| ut01 |
0.400 |
0.031 |
12.912 |
0 |
0.340 |
0.461 |
| ut02 |
0.299 |
0.026 |
11.270 |
0 |
0.247 |
0.350 |
| ut03 |
0.814 |
0.033 |
24.905 |
0 |
0.750 |
0.878 |
| ut04 |
0.760 |
0.035 |
21.825 |
0 |
0.692 |
0.829 |
| ut05 |
0.561 |
0.036 |
15.737 |
0 |
0.491 |
0.631 |
| ut06 |
0.420 |
0.032 |
13.219 |
0 |
0.357 |
0.482 |
| ut07 |
0.661 |
0.036 |
18.151 |
0 |
0.589 |
0.732 |
| ut08 |
0.584 |
0.036 |
16.237 |
0 |
0.514 |
0.655 |
| ut09 |
0.549 |
0.035 |
15.491 |
0 |
0.480 |
0.618 |
| ut10 |
1.000 |
0.001 |
696.420 |
0 |
0.997 |
1.003 |
| ut11 |
0.717 |
0.036 |
19.992 |
0 |
0.646 |
0.787 |
| ut12 |
0.507 |
0.035 |
14.700 |
0 |
0.440 |
0.575 |
| fa01 |
0.318 |
0.031 |
10.172 |
0 |
0.256 |
0.379 |
| fa02 |
0.871 |
0.030 |
29.210 |
0 |
0.812 |
0.929 |
| fa03 |
0.957 |
0.019 |
51.134 |
0 |
0.920 |
0.994 |
| fa04 |
0.855 |
0.031 |
27.568 |
0 |
0.794 |
0.916 |
| fa05 |
0.278 |
0.030 |
9.259 |
0 |
0.219 |
0.337 |
| fa06 |
0.616 |
0.038 |
16.300 |
0 |
0.542 |
0.691 |
| fa07 |
0.944 |
0.021 |
44.524 |
0 |
0.902 |
0.985 |
| fa08 |
0.883 |
0.029 |
30.690 |
0 |
0.826 |
0.939 |
| fa09 |
0.824 |
0.033 |
24.898 |
0 |
0.759 |
0.889 |
| fa10 |
0.653 |
0.038 |
17.275 |
0 |
0.579 |
0.727 |
| de01 |
0.666 |
0.037 |
18.067 |
0 |
0.594 |
0.738 |
| de02 |
0.509 |
0.035 |
14.429 |
0 |
0.439 |
0.578 |
| de03 |
0.639 |
0.037 |
17.319 |
0 |
0.567 |
0.712 |
| de04 |
0.793 |
0.034 |
23.283 |
0 |
0.726 |
0.859 |
| de05 |
0.728 |
0.036 |
20.177 |
0 |
0.657 |
0.798 |
| de06 |
0.614 |
0.037 |
16.662 |
0 |
0.542 |
0.686 |
| de07 |
0.618 |
0.037 |
16.762 |
0 |
0.546 |
0.690 |
| de08 |
0.524 |
0.036 |
14.717 |
0 |
0.454 |
0.593 |
| de09 |
0.922 |
0.024 |
38.260 |
0 |
0.874 |
0.969 |
| de10 |
0.512 |
0.035 |
14.486 |
0 |
0.442 |
0.581 |
| de11 |
0.961 |
0.018 |
54.124 |
0 |
0.926 |
0.995 |
| un01 |
0.438 |
0.031 |
13.890 |
0 |
0.376 |
0.499 |
| un02 |
0.319 |
0.026 |
12.139 |
0 |
0.267 |
0.370 |
| un03 |
0.695 |
0.036 |
19.530 |
0 |
0.626 |
0.765 |
| un04 |
0.443 |
0.032 |
13.978 |
0 |
0.381 |
0.505 |
| un05 |
0.349 |
0.028 |
12.588 |
0 |
0.295 |
0.404 |
| un06 |
0.814 |
0.032 |
25.176 |
0 |
0.750 |
0.877 |
| un07 |
0.514 |
0.034 |
15.159 |
0 |
0.448 |
0.581 |
| un08 |
0.422 |
0.031 |
13.656 |
0 |
0.362 |
0.483 |
| un09 |
0.510 |
0.034 |
15.093 |
0 |
0.444 |
0.577 |
| un10 |
0.483 |
0.033 |
14.616 |
0 |
0.418 |
0.548 |
| un11 |
0.402 |
0.030 |
13.356 |
0 |
0.343 |
0.461 |
| un12 |
0.446 |
0.032 |
14.021 |
0 |
0.384 |
0.508 |
| PR |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| CO |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| UT |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| FA |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| DE |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
| UN |
1.000 |
0.000 |
NA |
NA |
1.000 |
1.000 |
Visualization:
semPaths(model1, "std")

Validation
Data preparation